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- // Ceres Solver - A fast non-linear least squares minimizer
- // Copyright 2023 Google Inc. All rights reserved.
- // http://ceres-solver.org/
- //
- // Redistribution and use in source and binary forms, with or without
- // modification, are permitted provided that the following conditions are met:
- //
- // * Redistributions of source code must retain the above copyright notice,
- // this list of conditions and the following disclaimer.
- // * Redistributions in binary form must reproduce the above copyright notice,
- // this list of conditions and the following disclaimer in the documentation
- // and/or other materials provided with the distribution.
- // * Neither the name of Google Inc. nor the names of its contributors may be
- // used to endorse or promote products derived from this software without
- // specific prior written permission.
- //
- // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
- // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
- // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
- // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
- // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
- // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
- // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
- // POSSIBILITY OF SUCH DAMAGE.
- //
- // Author: sameeragarwal@google.com (Sameer Agarwal)
- //
- // Block structure objects are used to carry information about the
- // dense block structure of sparse matrices. The BlockSparseMatrix
- // object uses the BlockStructure objects to keep track of the matrix
- // structure and operate upon it. This allows us to use more cache
- // friendly block oriented linear algebra operations on the matrix
- // instead of accessing it one scalar entry at a time.
- #ifndef CERES_INTERNAL_BLOCK_STRUCTURE_H_
- #define CERES_INTERNAL_BLOCK_STRUCTURE_H_
- #include <cstdint>
- #include <vector>
- #include "ceres/internal/export.h"
- // This file is being included into source files that are compiled with nvcc.
- // nvcc shipped with ubuntu 20.04 does not support some features of c++17,
- // including nested namespace definitions
- namespace ceres {
- namespace internal {
- using BlockSize = int32_t;
- struct CERES_NO_EXPORT Block {
- Block() = default;
- Block(int size_, int position_) noexcept : size(size_), position(position_) {}
- BlockSize size{-1};
- int position{-1}; // Position along the row/column.
- };
- inline bool operator==(const Block& left, const Block& right) noexcept {
- return (left.size == right.size) && (left.position == right.position);
- }
- struct CERES_NO_EXPORT Cell {
- Cell() = default;
- Cell(int block_id_, int position_) noexcept
- : block_id(block_id_), position(position_) {}
- // Column or row block id as the case maybe.
- int block_id{-1};
- // Where in the values array of the jacobian is this cell located.
- int position{-1};
- };
- // Order cell by their block_id;
- CERES_NO_EXPORT bool CellLessThan(const Cell& lhs, const Cell& rhs);
- struct CERES_NO_EXPORT CompressedList {
- CompressedList() = default;
- // Construct a CompressedList with the cells containing num_cells
- // entries.
- explicit CompressedList(int num_cells) noexcept : cells(num_cells) {}
- Block block;
- std::vector<Cell> cells;
- // Number of non-zeros in cells of this row block
- int nnz{-1};
- // Number of non-zeros in cells of this and every preceeding row block in
- // block-sparse matrix
- int cumulative_nnz{-1};
- };
- using CompressedRow = CompressedList;
- using CompressedColumn = CompressedList;
- // CompressedRowBlockStructure specifies the storage structure of a row block
- // sparse matrix.
- //
- // Consider the following matrix A:
- // A = [A_11 A_12 ...
- // A_21 A_22 ...
- // ...
- // A_m1 A_m2 ... ]
- //
- // A row block sparse matrix is a matrix where the following properties hold:
- // 1. The number of rows in every block A_ij and A_ik are the same.
- // 2. The number of columns in every block A_ij and A_kj are the same.
- // 3. The number of rows in A_ij and A_kj may be different (i != k).
- // 4. The number of columns in A_ij and A_ik may be different (j != k).
- // 5. Any block A_ij may be all 0s, in which case the block is not stored.
- //
- // The structure of the matrix is stored as follows:
- //
- // The `rows' array contains the following information for each row block:
- // - rows[i].block.size: The number of rows in each block A_ij in the row block.
- // - rows[i].block.position: The starting row in the full matrix A of the
- // row block i.
- // - rows[i].cells[j].block_id: The index into the `cols' array corresponding to
- // the non-zero blocks A_ij.
- // - rows[i].cells[j].position: The index in the `values' array for the contents
- // of block A_ij.
- //
- // The `cols' array contains the following information for block:
- // - cols[.].size: The number of columns spanned by the block.
- // - cols[.].position: The starting column in the full matrix A of the block.
- //
- //
- // Example of a row block sparse matrix:
- // block_id: | 0 |1|2 |3 |
- // rows[0]: [ 1 2 0 3 4 0 ]
- // [ 5 6 0 7 8 0 ]
- // rows[1]: [ 0 0 9 0 0 0 ]
- //
- // This matrix is stored as follows:
- //
- // There are four column blocks:
- // cols[0].size = 2
- // cols[0].position = 0
- // cols[1].size = 1
- // cols[1].position = 2
- // cols[2].size = 2
- // cols[2].position = 3
- // cols[3].size = 1
- // cols[3].position = 5
- // The first row block spans two rows, starting at row 0:
- // rows[0].block.size = 2 // This row block spans two rows.
- // rows[0].block.position = 0 // It starts at row 0.
- // rows[0] has two cells, at column blocks 0 and 2:
- // rows[0].cells[0].block_id = 0 // This cell is in column block 0.
- // rows[0].cells[0].position = 0 // See below for an explanation of this.
- // rows[0].cells[1].block_id = 2 // This cell is in column block 2.
- // rows[0].cells[1].position = 4 // See below for an explanation of this.
- //
- // The second row block spans two rows, starting at row 2:
- // rows[1].block.size = 1 // This row block spans one row.
- // rows[1].block.position = 2 // It starts at row 2.
- // rows[1] has one cell at column block 1:
- // rows[1].cells[0].block_id = 1 // This cell is in column block 1.
- // rows[1].cells[0].position = 8 // See below for an explanation of this.
- //
- // The values in each blocks are stored contiguously in row major order.
- // However, there is no unique way to order the blocks -- it is usually
- // optimized to promote cache coherent access, e.g. ordering it so that
- // Jacobian blocks of parameters of the same type are stored nearby.
- // This is one possible way to store the values of the blocks in a values array:
- // values = { 1, 2, 5, 6, 3, 4, 7, 8, 9 }
- // | | | | // The three blocks.
- // ^ rows[0].cells[0].position = 0
- // ^ rows[0].cells[1].position = 4
- // ^ rows[1].cells[0].position = 8
- struct CERES_NO_EXPORT CompressedRowBlockStructure {
- std::vector<Block> cols;
- std::vector<CompressedRow> rows;
- };
- struct CERES_NO_EXPORT CompressedColumnBlockStructure {
- std::vector<Block> rows;
- std::vector<CompressedColumn> cols;
- };
- inline int NumScalarEntries(const std::vector<Block>& blocks) {
- if (blocks.empty()) {
- return 0;
- }
- auto& block = blocks.back();
- return block.position + block.size;
- }
- std::vector<Block> Tail(const std::vector<Block>& blocks, int n);
- int SumSquaredSizes(const std::vector<Block>& blocks);
- } // namespace internal
- } // namespace ceres
- #endif // CERES_INTERNAL_BLOCK_STRUCTURE_H_
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